Uses of Class
org.deeplearning4j.models.paragraphvectors.ParagraphVectors.Builder

Packages that use ParagraphVectors.Builder

Package	Description
org.deeplearning4j.models.paragraphvectors

Uses of ParagraphVectors.Builder in org.deeplearning4j.models.paragraphvectors

Methods in org.deeplearning4j.models.paragraphvectors that return ParagraphVectors.Builder

Modifier and Type	Method	Description
ParagraphVectors.Builder	ParagraphVectors.Builder.allowParallelTokenization(boolean allow)	This method enables/disables parallel tokenization.
ParagraphVectors.Builder	ParagraphVectors.Builder.batchSize(int batchSize)	This method defines mini-batch size
ParagraphVectors.Builder	ParagraphVectors.Builder.elementsLearningAlgorithm(String algorithm)
ParagraphVectors.Builder	ParagraphVectors.Builder.elementsLearningAlgorithm(ElementsLearningAlgorithm<VocabWord> algorithm)
ParagraphVectors.Builder	ParagraphVectors.Builder.enableScavenger(boolean reallyEnable)	This method ebables/disables periodical vocab truncation during construction Default value: disabled
ParagraphVectors.Builder	ParagraphVectors.Builder.epochs(int numEpochs)	This method defines number of epochs (iterations over whole training corpus) for training
ParagraphVectors.Builder	ParagraphVectors.Builder.iterate(@NonNull SequenceIterator<VocabWord> iterator)	This method used to feed SequenceIterator, that contains training corpus, into ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.iterate(@NonNull DocumentIterator iterator)	This method used to feed DocumentIterator, that contains training corpus, into ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.iterate(@NonNull LabelAwareDocumentIterator iterator)	This method used to feed LabelAwareDocumentIterator, that contains training corpus, into ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.iterate(@NonNull LabelAwareIterator iterator)	This method used to feed LabelAwareIterator, that contains training corpus, into ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.iterate(@NonNull LabelAwareSentenceIterator iterator)	This method used to feed LabelAwareSentenceIterator, that contains training corpus, into ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.iterate(@NonNull SentenceIterator iterator)	This method used to feed SentenceIterator, that contains training corpus, into ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.iterations(int iterations)	This method defines number of iterations done for each mini-batch during training
ParagraphVectors.Builder	ParagraphVectors.Builder.labels(@NonNull List<String> labels)	Deprecated.
ParagraphVectors.Builder	ParagraphVectors.Builder.labelsSource(@NonNull LabelsSource source)	This method attaches pre-defined labels source to ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.layerSize(int layerSize)	This method defines number of dimensions for output vectors
ParagraphVectors.Builder	ParagraphVectors.Builder.learningRate(double learningRate)	This method defines initial learning rate for model training
ParagraphVectors.Builder	ParagraphVectors.Builder.limitVocabularySize(int limit)	This method sets vocabulary limit during construction.
ParagraphVectors.Builder	ParagraphVectors.Builder.lookupTable(@NonNull WeightLookupTable<VocabWord> lookupTable)	This method allows to define external WeightLookupTable to be used
ParagraphVectors.Builder	ParagraphVectors.Builder.minLearningRate(double minLearningRate)	This method defines minimal learning rate value for training
ParagraphVectors.Builder	ParagraphVectors.Builder.minWordFrequency(int minWordFrequency)	This method defines minimal word frequency in training corpus.
ParagraphVectors.Builder	ParagraphVectors.Builder.modelUtils(@NonNull ModelUtils<VocabWord> modelUtils)	Sets ModelUtils that gonna be used as provider for utility methods: similarity(), wordsNearest(), accuracy(), etc
ParagraphVectors.Builder	ParagraphVectors.Builder.negativeSample(double negative)	This method defines whether negative sampling should be used or not PLEASE NOTE: If you're going to use negative sampling, you might want to disable HierarchicSoftmax, which is enabled by default Default value: 0
ParagraphVectors.Builder	ParagraphVectors.Builder.resetModel(boolean reallyReset)	This method defines whether model should be totally wiped out prior building, or not
ParagraphVectors.Builder	ParagraphVectors.Builder.sampling(double sampling)	This method defines whether subsampling should be used or not
ParagraphVectors.Builder	ParagraphVectors.Builder.seed(long randomSeed)	This method defines random seed for random numbers generator
ParagraphVectors.Builder	ParagraphVectors.Builder.sequenceLearningAlgorithm(String algorithm)
ParagraphVectors.Builder	ParagraphVectors.Builder.sequenceLearningAlgorithm(SequenceLearningAlgorithm<VocabWord> algorithm)
ParagraphVectors.Builder	ParagraphVectors.Builder.setVectorsListeners(@NonNull Collection<VectorsListener<VocabWord>> vectorsListeners)	This method sets VectorsListeners for this SequenceVectors model
ParagraphVectors.Builder	ParagraphVectors.Builder.stopWords(@NonNull Collection<VocabWord> stopList)	This method defines stop words that should be ignored during training
ParagraphVectors.Builder	ParagraphVectors.Builder.stopWords(@NonNull List<String> stopList)	This method defines stop words that should be ignored during training
ParagraphVectors.Builder	ParagraphVectors.Builder.tokenizerFactory(@NonNull TokenizerFactory tokenizerFactory)	This method defines TokenizerFactory to be used for strings tokenization during training PLEASE NOTE: If external VocabCache is used, the same TokenizerFactory should be used to keep derived tokens equal.
ParagraphVectors.Builder	ParagraphVectors.Builder.trainElementsRepresentation(boolean trainElements)	This method defines, if words representation should be build together with documents representations.
ParagraphVectors.Builder	ParagraphVectors.Builder.trainSequencesRepresentation(boolean trainSequences)	This method is hardcoded to TRUE, since that's whole point of ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.trainWordVectors(boolean trainElements)	This method defines, if words representations should be build together with documents representations.
ParagraphVectors.Builder	ParagraphVectors.Builder.unknownElement(VocabWord element)	This method allows you to specify SequenceElement that will be used as UNK element, if UNK is used
ParagraphVectors.Builder	ParagraphVectors.Builder.useAdaGrad(boolean reallyUse)	This method defines whether adaptive gradients should be used or not
ParagraphVectors.Builder	ParagraphVectors.Builder.useExistingWordVectors(@NonNull WordVectors vec)	This method allows you to use pre-built WordVectors model (e.g.
ParagraphVectors.Builder	ParagraphVectors.Builder.useHierarchicSoftmax(boolean reallyUse)	This method enables/disables Hierarchic softmax Default value: enabled
ParagraphVectors.Builder	ParagraphVectors.Builder.usePreciseWeightInit(boolean reallyUse)
ParagraphVectors.Builder	ParagraphVectors.Builder.useUnknown(boolean reallyUse)	This method allows you to specify, if UNK word should be used internally
ParagraphVectors.Builder	ParagraphVectors.Builder.useVariableWindow(int... windows)	This method has no effect for ParagraphVectors
ParagraphVectors.Builder	ParagraphVectors.Builder.vocabCache(@NonNull VocabCache<VocabWord> vocabCache)	This method allows to define external VocabCache to be used
ParagraphVectors.Builder	ParagraphVectors.Builder.windowSize(int windowSize)	This method defines context window size
ParagraphVectors.Builder	ParagraphVectors.Builder.workers(int numWorkers)	This method defines maximum number of concurrent threads available for training